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Learn to build controllable AI agents using LangGraph. Create agents from scratch, implement persistence, incorporate human-in-the-loop, and develop an essay-writing agent. Enhance agent knowledge with agentic search.
Gain deep understanding of diffusion models in AI. Build from scratch, explore sampling, training, and personalized image generation. Hands-on labs with Jupyter notebooks expand your generative AI skills.
Master in-demand data analytics skills with practical, real-world applications. Learn to manage the entire data lifecycle while integrating AI tools to accelerate workflows and extract meaningful insights for better decision-making.
Explore AI's role in public health, learn problem-solving frameworks, and create an air quality monitoring app for Bogotá in hands-on labs.
Gain insights into data analytics fundamentals, from understanding raw data to creating impactful visualizations using spreadsheets. Learn structured approaches for analysis projects while exploring real-world datasets and leveraging LLMs.
Master building production-ready RAG systems that connect LLMs to external data sources through hands-on labs with vector databases, retrieval methods, and real-world datasets.
Explore AI's role in climate change mitigation through wind power forecasting and biodiversity monitoring case studies, gaining practical skills in time series analysis and computer vision.
Explore source systems, implement batch and streaming pipelines, automate data workflows, and monitor data quality using AWS and open-source tools for efficient data management.
Explore Hugging Face's open-source models for NLP, audio, image, and multimodal tasks. Learn to create chatbots, translate languages, process audio, classify images, and build user-friendly AI apps using Gradio and Hugging Face Spaces.
Explore LangChain's latest features: function calling, LCEL syntax, and tool selection. Build advanced conversational agents and enhance your LLM applications with structured outputs and efficient customization.
Master PyTorch fundamentals, advanced architectures, and deployment techniques to build, train, and optimize deep learning models for computer vision and NLP applications.
Learn to leverage knowledge graphs in RAG applications, enhancing LLM performance with structured data relationships, advanced querying, and comprehensive information retrieval techniques.
Model, transform, and serve data for analytics and machine learning. Explore data modeling techniques, use dbt, compare architectures, and learn about distributed processing frameworks and stream processing.
Learn to build intelligent research agents using LlamaIndex, enhancing data analysis with tool use, reasoning, and decision-making capabilities across multiple documents.
Master PyTorch fundamentals through hands-on coding, building neural networks from single neurons to CNNs, managing datasets, and implementing complete ML workflows with confidence.
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